Existing methods used for the timing analysis of embedded systems include analytical, simulation-based and stochastic methods. These methods provide imprecise analysis. Analytical methods using schedulability analysis and Real Time Calculus give safe approximations. However since they cannot handle some operational details of the system under consideration and do not compute the reachable states in a model of the system, the results can be very pessimistic. Stochastic methods are good for average case analysis, but are not suitable for worst-case analysis. Further, these methods do not allow deterministic modeling of arbitrary scheduling algorithms and controller buffer policies, which influence actual results. Additionally, existing analytical and stochastic analysis tools do, not provide good solutions for timing synchronization problems, because these problems involve simultaneous analysis of multiple event chains which are beyond the capabilities of these methods. Simulation based methods can handle operational details. However, they do not guarantee coverage of corner cases during system simulation and timing measurements, thus possibly giving optimistic results.
Formal methods-based tools and methods for timing analysis exist but have not been scalable to large industrial examples. For example, formal methods based on timed automata cannot address the large amount of data associated with a complex system and typically fail due to the large memory and time required to complete the analysis. With the increasing complexity of electrical systems, such as automotive electrical systems comprising multiple electronic control units (ECUs) communicating via multiple controller area network (CAN) buses, scalable timing analysis methods and tools capable of analyzing these complex systems with precision and accuracy are needed.